In this session, we present the first systematic study of quantum support vector machine (QSVM) complexity space and the first quantum classification of an electronic health records dataset. We classified the persistence of rheumatoid arthritis patients on biologic therapies, predicting 6-month persistence via binary classification. In addition, we developed an end-to-end framework to study empirical quantum advantage that can be generalized for other machine learning and optimization problems. This was achieved by comparing the landscapes of classical and quantum models via introduction of the terrain ruggedness index. We selected data subsets and created a grid of 5–20 features and 200–300 samples. For each grid coordinate (number of features, number of training samples), we trained classical SVM models based on radial basis function kernels and quantum models with custom kernels using Qiskit and IBM Quantum simulators and real hardware. We observed partial empirical quantum advantage and our generalizable framework enables a priori identification of datasets where quantum advantage could exist.